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data_factory.R
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#!/usr/bin/Rscript
###Set VERSION
VERSION = "1.0.5"
args <- commandArgs(trailingOnly = TRUE)
if(length(args) == 1 && (args[1] == "-v" | args[1] == "--version")){
message("scRNA seurat pipeline\nVersion: \n\t", VERSION, "\n")
quit(status = 0)
}
suppressPackageStartupMessages(library(optparse)) ## Options
suppressPackageStartupMessages(library(futile.logger)) ## logger
suppressPackageStartupMessages(library(dplyr))
`%ni%` <- Negate(`%in%`)
### TODO LIST
#--- save the parameter into R object
#--- 1v1 cluster name set as a parameter should be good
AllOptions <- function(){
parser <- OptionParser()
parser <- add_option(parser, c("-n", "--numberofcores"), type="integer", default=1,
help="Parallel Run Number of cores [default %default]",
metavar="number")
parser <- add_option(parser, c("--MaxMemMega"), type="integer", default=20000,
help="Parallel Max Memory size megabytes [default %default]",
metavar="number")
parser <- add_option(parser, c("-c", "--configfile"), type="character", default="conf/config.R",
help="Config file for the run, input files and executing plan settings [default %default]",
metavar="character")
parser <- add_option(parser, c("-l", "--logname"), type="character", default="",
help="add a unique name for log files [default %default]",
metavar="character")
parser <- add_option(parser, c("--nFeatureRNAfloor"), type="character", default=400,
help="min nFeature_RNA [default %default]",
metavar="number")
parser <- add_option(parser, c("--nFeatureRNAceiling"), type="character", default=Inf,
help="max nFeature_RNA [default %default]",
metavar="number")
parser <- add_option(parser, c("--nCountRNAfloor"), type="character", default=0,
help="min nCount_RNA [default %default]",
metavar="number")
parser <- add_option(parser, c("--nCountRNAceiling"), type="character", default=40000,
help="max nCount_RNA [default %default]",
metavar="number")
parser <- add_option(parser, c("--pct_mitofloor"), type="character", default=-Inf,
help="min percentage mito [default %default]",
metavar="number")
parser <- add_option(parser, c("--pct_mitoceiling"), type="character", default=100,
help="max percentage mito [default %default]",
metavar="number")
parser <- add_option(parser, c("--pct_ribofloor"), type="character", default=-Inf,
help="min percentage ribo [default %default]",
metavar="number")
parser <- add_option(parser, c("--pct_riboceiling"), type="character", default=100,
help="max percentage ribo [default %default]",
metavar="number")
parser <- add_option(parser, c("-f", "--countmatrixformat"), type="character", default="10X",
help="count matrix format, current support 10X and 10X_h5 [default %default]",
metavar="character")
parser <- add_option(parser, c("-s", "--savedir"), type="character", default="save",
help="RObject save directory [default %default]",
metavar="character")
parser <- add_option(parser, c("-e", "--chartsdir"), type="character", default="charts",
help="xlsx directory [default %default]",
metavar="character")
parser <- add_option(parser, c("-d", "--dims4Integrate"), type="character", default="1:30",
help="Dims to keep for integrating [default %default]",
metavar="VECTOR")
parser <- add_option(parser, c("-x", "--Dims4FindNeighbors"), type="character", default="1:50",
help="Dims kept for findNeighbors [default %default]",
metavar="VECTOR")
parser <- add_option(parser, c("-r", "--clusterresolution"), type="numeric", default=0.5,
help="Resolution for clustering [default %default]",
metavar="numeric")
parser <- add_option(parser, c("-a", "--defaultclustername"), type="character", default="seurat_clusters",
help="downstream analysis default cluster name [default %default]",
metavar="character")
parser <- add_option(parser, c("--harmony_dim"), type="character", default="1:50",
help="dims keep to do clustering using harmony [default %default]",
metavar="character")
parser <- add_option(parser, c("--allinone"), type="logical", default=TRUE,
help="store calculated result in scrna@tools & assays in scrna@assays if TRUE, else store in save/partition & save/assays respectively [default %default]",
metavar="character")
parser <- add_option(
parser, c("-z", "--compression"),
type = "character", default = "gzip", metavar = "character",
help = paste0(
"Compression algorithm to use when saving R objects [default %default]. ",
"One of 'zstd', 'lz4', 'gzip', 'bzip2', ",
"'xz' or 'nocomp' (no compression)"
)
)
return(parser)
}
parser <- AllOptions()
#debug(parse_args)
pa <- parse_args(parser)
WORKER_NUM = pa$numberofcores
MAXMEMMEGA = pa$MaxMemMega
SAVE_DIR = pa$savedir
CHARTS_DIR = pa$chartsdir
INTEGRATED_DIM = eval(parse(text=pa$dims4Integrate))
HARMONY_DIM = eval(parse(text=pa$harmony_dim))
FINDNEIGHBORS_DIM = eval(parse(text=pa$Dims4FindNeighbors))
CLUSTER_RESOLUTION = pa$clusterresolution
DEFUALT_CLUSTER_NAME = pa$defaultclustername
CM_FORMAT = pa$countmatrixformat
COMPRESSION_FORMAT = pa$compression
ALLINONE = pa$allinone
f = function(x){if(x == ""){ return("")}else{ return("-")}}
LOGNAME = paste0(pa$logname, f(pa$logname))
toInt <- function(x) switch(x, "Inf"=Inf, "-Inf" = -Inf, as.integer(x))
nFeatureRNAfloor = toInt(pa$nFeatureRNAfloor)
nFeatureRNAceiling = toInt(pa$nFeatureRNAceiling)
nCountRNAceiling = toInt(pa$nCountRNAceiling)
nCountRNAfloor = toInt(pa$nCountRNAfloor)
pct_mitoceiling = toInt(pa$pct_mitoceiling)
pct_mitofloor = toInt(pa$pct_mitofloor)
pct_riboceiling = toInt(pa$pct_riboceiling)
pct_ribofloor = toInt(pa$pct_ribofloor)
CLUSTER_RESOLUTION_RANGE = seq(0.1, 0.8, 0.1)
if(!file.exists("logs")){
dir.create("logs")
}
cur_date <- as.character(Sys.Date())
errorLog = file.path("logs", sprintf("%sERROR-%s.log", LOGNAME, cur_date))
warnLog = file.path("logs", sprintf("%sWARN-%s.log", LOGNAME, cur_date))
infoLog = file.path("logs", sprintf("%sINFO-%s.log", LOGNAME, cur_date))
traceLog = file.path("logs", sprintf("%sTRAC-%s.log", LOGNAME, cur_date))
invisible(flog.logger("error", ERROR, appender.file(errorLog)))
invisible(flog.logger("warn", WARN, appender.file(warnLog)))
invisible(flog.logger("info", INFO, appender.file(infoLog)))
invisible(flog.logger("trace", TRACE, appender.file(traceLog)))
invisible(flog.appender(appender.console(), name = "ROOT"))
logger.info <- function(msg, ...) {
flog.info(msg, ..., name = "ROOT")
flog.info(msg, ..., name = "info")
}
logger.warn <- function(msg, ...) {
flog.warn(msg, ..., name = "ROOT")
flog.warn(msg, ..., name = "info")
flog.warn(msg, ..., name = "warn")
}
logger.error <- function(msg, ...) {
flog.error(msg, ..., name = "ROOT")
flog.error(msg, ..., name = "info")
flog.error(msg, ..., name = "warn")
flog.error(msg, ..., name = "error")
flog.error(msg, ..., name = "trace")
}
## Robject directory
if(!file.exists(SAVE_DIR)){
dir.create(SAVE_DIR)
logger.info("RObject save in directory: %s", SAVE_DIR)
}else{
logger.info("RObject save in existed directory: %s", SAVE_DIR)
logger.info("meta.data save in directory: %s", file.path(SAVE_DIR))
}
if(!file.exists(file.path(SAVE_DIR, "meta"))){
dir.create(file.path(SAVE_DIR, "meta"), showWarnings=F)
logger.info("meta.data save in directory: %s", file.path(SAVE_DIR, "meta"))
}
if(!ALLINONE){
dir.create(file.path(SAVE_DIR, "partition"), showWarnings=F)
logger.info("data in @tools save in directory: %s/partition", SAVE_DIR)
dir.create(file.path(SAVE_DIR, "assays"), showWarnings=F)
logger.info("data in assays save in directory: %s/assays", SAVE_DIR)
}
if(!file.exists(CHARTS_DIR)){
dir.create(CHARTS_DIR)
logger.info("xlsx save in directory: %s", CHARTS_DIR)
}else{
logger.info("xlsx save in existed directory: %s", CHARTS_DIR)
}
## configuration file
if(!file.exists(pa$configfile)){
logger.error("Please check the config file!")
print("Please check the config file!")
parse_args(parser, args = c("--help"))
}
source(pa$configfile)
source("R/save_load_helper.R")
##--------------Load the function scripts----------------------
R_scripts <- list.files("R_scripts/", full.name = TRUE)
for(i in 1:length(R_scripts)) source(R_scripts[i])
##--------------keep variables----------------------
pa$project_name = PROJECT
pa$organ = ORGAN
pa$species = SPECIES
pa$mca_name = MCA_NAME
pa$hcl_name = HCL_NAME
pa$external_file = ANNOTATION_EXTERNAL_FILE
pa$data_src = data_src
pa$stage_lst = stage_lst
pa$integration_option = INTEGRATION_OPTION
pa$doublet_switch = doublet_switch
pa$doublet_lst = doublet_lst
if (!setequal(names(data_src), names(stage_lst))){
logger.error("names of `data_src` and `stage_lst` are not consistent, please check your configuration file!")
stop("names of data_src and stage_lst are not consistent!!!!")
}
if(doublet_switch=="on"| doublet_switch=="display"){
if (!setequal(names(data_src), names(doublet_lst))){
logger.error("names of `data_src` and `doublet_lst` are not consistent, please check your configuration file!")
stop("names of data_src and doublet_lst are not consistent!!!!")
}
}
##--------------Loading the relevant slots and attributes for the sanity function----------------------
sanity_attributes <- read.table("static/SlotsAttributes.csv", sep = ";", header = TRUE, stringsAsFactors = FALSE)
##--------------Doublet estimation----------------------
estimation_10X <- read.csv("static/DoubletEstimation10X.csv")
model_recovered <- approxfun(x = estimation_10X$CellsRecovered, y = estimation_10X$MultipletRate, rule = 2)
logger.info("----------------------Project: %s --------------------------", PROJECT)
## src data summary
logger.info("samples summary: ")
for(i in seq_along(data_src)){
key = names(data_src)[i]
val = data_src[[key]]
logger.info(paste(i, key, val, sep= " -------> "))
}
plan_record <- function(plan){
start_line <- "\n\n======================================================================="
rfile <- "logs/plans.log"
write(start_line, rfile, append=TRUE)
write(as.character(Sys.time()), rfile, append=TRUE)
write("Parameters:", rfile, append=TRUE)
for (nm in names(pa)) {
write(sprintf("\t\t\t%s: %s", nm, pa[nm]), rfile, append=TRUE)
}
write("\nplan:", rfile, append=TRUE)
for (x in plan) {
write(x, rfile, append=TRUE)
}
}
# setup function to save object to debug for ease of use
debug_save_stop <- function(scrna,key){
save_object(
object = scrna,
file_name = file.path(SAVE_DIR, "scrna_for_debug.Rds"),
file_format = COMPRESSION_FORMAT
)
stop(glue("ERROR when run {key}"))
}
## executing plan
conf = conf[conf > 0]
if(conf[1] != 2 & (names(conf)[1] %ni% c("scrna_phase_preprocess", "scrna_phase_singleton", "scrna_rawdata"))){
logger.error("!!Run without loading data, please check execution plan")
stop("exit 1")
}
orig_len <- length(conf)
remove_useless_loadings <- function(conf){
leng <- length(conf)
if(conf[1] != 2){ ## no loading
return(conf[1:leng])
}
for(i in 1:length(conf)){
if(conf[i] == 2 ){ next }
else{return(conf[(i-1):leng]) }
}
return(conf[1:leng])
}
conf <- remove_useless_loadings(conf)
if(orig_len > length(conf)){
logger.warn("Conf set too many loading Rds, will not load useless ones!")
}
dic = c("1"="calculating", "2"="loading")
plan = sprintf("\t\t\t%s: %s", dic[as.character(conf)], names(conf))
logger.info("The executing plan is: ")
logger.info(plan)
plan_record(plan)
## TODO program...
## Just One Sample should be taken into account
# library roxygen2 -> to maintain documents
suppressPackageStartupMessages(library(Seurat))
suppressPackageStartupMessages(library(progeny))
suppressPackageStartupMessages(library(glue))
suppressPackageStartupMessages(library(future.apply)) ## parallel
suppressPackageStartupMessages(library(WriteXLS))
suppressPackageStartupMessages(library(clustree))
suppressPackageStartupMessages(library(Matrix))
suppressPackageStartupMessages(library(data.table))
suppressPackageStartupMessages(library(stringr))
suppressPackageStartupMessages(library(Hmisc))
suppressPackageStartupMessages(library(foreach))
suppressPackageStartupMessages(library(doParallel))
suppressPackageStartupMessages(library(celda))
suppressPackageStartupMessages(library(SoupX))
suppressPackageStartupMessages(library(Rmagic))
suppressPackageStartupMessages(library(DoubletFinder))
suppressPackageStartupMessages(library(SeuratWrappers))
registerDoParallel(cores=WORKER_NUM)
# Set up future for parallelization
plan("multicore", workers = WORKER_NUM)
options(future.globals.maxSize = MAXMEMMEGA * 1024^2)
cur_time <- as.character(Sys.time())
conf_main <- function(){
scrna = NULL
cluster.de = NULL
for(i in seq_along(conf)){
key = names(conf)[i]
val = conf[i]
NRds = paste(key, ".Rds", sep="")
if(val==0){
# do nothing
}else if(val==2){
logger.info(paste("loading", NRds))
scrna <- load_object(file_name = file.path(SAVE_DIR, NRds))
scrna@tools$parameter[[cur_time]] <- unlist(pa)
scrna@tools$execution[[cur_time]] <- conf
scrna <- sanity_function(scrna, key)
scrna@tools$allinone = ALLINONE
logger.info(paste("finished loading", NRds))
}else if(val==1){
func_name = paste("generate_", key, sep="")
func_call = paste(func_name, "(scrna)", sep="")
logger.info(paste("executing", func_name))
if(startsWith(key, "scrna")){
ret_list <- eval(parse(text=func_call))
scrna <- ret_list[[1]]
ret_code <- ret_list[[2]]
scrna <- sanity_function(scrna, key)
if(ret_code != 0){ debug_save_stop(scrna, key) }
#if("meta_order" %ni% names(scrna@tools)){
scrna@tools[["meta_order"]] <- list(
name = names(data_src),
stage = unique(stage_lst)
)
scrna@tools$allinone = ALLINONE
#}
phase_name <- get_output_name(scrna, key) ### only first store, or second time
save_object(
object = scrna,
file_name = file.path(SAVE_DIR, glue("{phase_name}.Rds")),
file_format = COMPRESSION_FORMAT
)
save_object(
object = scrna@meta.data,
file_name = file.path(SAVE_DIR, 'meta', glue("{phase_name}_meta.Rds")),
file_format = COMPRESSION_FORMAT
)
logger.info(paste("finished", phase_name))
}
}else{
logger.error("Wrong settings in config file in section RUN PARAMETERS\n Only 0, 1 or 2 permitted")
logger.error(traceback())
stop("Exit 1")
}
}
logger.info("===============Finished===============")
}
get_regressout_vector <- function(){
dic <- list(
"mito" = "percent.mt",
"ribo" = "percent.ribo",
"cellcycle" = c("G2M.Score", "S.Score"))
keep <- names(preprocess_regressout[preprocess_regressout==1])
ro <- unlist(dic[keep])
names(ro) <- NULL
return(ro)
}
get_output_name<- function(scrna, scrna_run_name){
ret_code <- 0
pconf <- configr::read.config("static/phase.ini")
phases <- names(pconf)
if(scrna_run_name %in% paste0("scrna_", phases)){
return(scrna_run_name)
}else if(length(unique(scrna$name)) == 1){
return("scrna_phase_singleton")
}else{
return("scrna_phase_comparing")
}
}
dego_dump <- function(file_predix, de.list, go_ups, go_downs) {
#file_predix <- paste0(a_pair[1], ".vs.", a_pair[2])
flist <- lapply(de.list, subset, subset = p_val_adj < 0.05)
if(length(flist) == 0){
return()
}
flist <- flist[sapply(flist, function(m) nrow(m) >0)]
WriteXLS(
flist,
file.path(CHARTS_DIR, sprintf("%s.de_%s.xlsx", file_predix, DEFUALT_CLUSTER_NAME)),
SheetNames = reformat_sheetnames(names(flist)))
golist_xls(go_ups, sprintf("%s.goup_%s.xlsx",file_predix, DEFUALT_CLUSTER_NAME))
golist_xls(go_downs,sprintf("%s.godown_%s.xlsx",file_predix, DEFUALT_CLUSTER_NAME))
}
pathway_dump <- function(file_predix, ptype, ups, downs) {
golist_xls(ups, glue("{file_predix}.{ptype}up_{DEFUALT_CLUSTER_NAME}.xlsx"))
golist_xls(downs,glue("{file_predix}.{ptype}down_{DEFUALT_CLUSTER_NAME}.xlsx"))
}
list_1vs1 <- function(){
m <- combn(names(data_src), 2)
n <- length(m)/2
lst <- vector("list", n)
for (i in 1:n){
lst[[i]] <- m[1:2, i]
}
return(lst)
}
list_stages <- function(){
conds <- unique(stage_lst)
m <- combn(conds, 2)
n = length(m)/2
lst <- vector("list", n)
for (i in 1:n){
lst[[i]] <- m[1:2, i]
}
return(lst)
}
golist_xls <- function(go.list, fxls){
suppressPackageStartupMessages(require(ggplot2))
lst = lapply(go.list, function(x){
if(length(x) == 0)
return(as.data.frame(list()))
else
return(x)
})
if(length(lst) == 0){
logger.warn("GO LIST is EMPTY")
return(1)
}
#go.xls.list = lapply(lst, fortify, showCategory=Inf)
go.xls.list <- lapply(names(lst), function(x) lst[[x]]@result)
names(go.xls.list) = names(go.list)
WriteXLS(
go.xls.list,
file.path(CHARTS_DIR, fxls),
SheetNames = reformat_sheetnames(names(go.xls.list)))
}
reformat_sheetnames <- function(sheet_names){
## []:*?/\
sheet_names <- stringr::str_replace(sheet_names, "\\[", ".")
sheet_names <- stringr::str_replace(sheet_names, "\\]", ".")
sheet_names <- stringr::str_replace(sheet_names, ":", ".")
sheet_names <- stringr::str_replace(sheet_names, "/", ".")
sheet_names <- stringr::str_replace(sheet_names, "\\?", ".")
sheet_names <- stringr::str_replace(sheet_names, "\\\\", ".")
return(sheet_names)
}
generate_scrna_rawdata <- function(scrna){
ret_code = 0
tryCatch(
{
data.list = list()
read_func <- ifelse(CM_FORMAT == "10X", Read10X, Read10X_h5)
for(i in seq_along(data_src)){
print(paste(i, Sys.time()))
key = names(data_src)[i]
val = data_src[[key]]
data <- read_func(file.path(data_src[[key]]))
scrna <- CreateSeuratObject(counts = data, min.cells = MINCELLS, min.features = MINGENES, project = PROJECT)
name = names(data_src)[i]
scrna@meta.data[, "name"] <- name
scrna@meta.data[, "stage"] <- stage_lst[name]
data.list[[i]] <- scrna
rm(scrna)
}
scrna <- data.list[[1]]
if(length(data.list) > 1){
scrna <- merge(x = data.list[[1]], y = unlist(data.list[2:length(data.list)]),
merge.data = FALSE, add.cell.ids = names(data_src), project = PROJECT)
}
scrna[["percent.mt"]] <- PercentageFeatureSet(scrna, pattern = "^mt-|^MT-")
scrna[["percent.ribo"]] <- PercentageFeatureSet(scrna, pattern = "^Rpl|^Rps|^RPL|^RPS")
scrna@tools[["meta_order"]] <- list(name = names(data_src), stage=unique(stage_lst))
},
error=function(cond) {
ret_code <<- -1
logger.error(cond)
logger.error(traceback())
},
finally={
rm(data.list)
return(list(scrna, ret_code))
})
return(list(scrna, ret_code))
}
generate_scrna_ambient_rna <- function(scrna){
ret_code <- 0
tryCatch(
{
# We convert the input SeuratObject to a SingleCellExperiment object
# This is the require input for decontX
assay.used <- DefaultAssay(scrna)
DefaultAssay(scrna) <- "RNA"
scrna.sce <- as.SingleCellExperiment(scrna)
# Now, we estimate and correct the amount of ambient RNA.
# Since we have not given the function a clustering result,
# decontX will do the clustering for us.
# Since contamination is dependent on the specific experiment, we provide the decontX function with
# the batch information. The contamination is then calculated per sample.
scrna.decont <- decontX(scrna.sce, batch = scrna$name)
# We add the estimated contamination and the decontaminated data to the SeuratObject
scrna[["decontX"]] <- CreateAssayObject(counts = scrna.decont@assays@data$decontXcounts)
scrna <- AddMetaData(object = scrna, metadata = scrna.decont$decontX_contamination,
col.name = "AmbientRNA")
scrna <- AddMetaData(object = scrna, metadata = scrna.decont$decontX_clusters,
col.name = "decontX_clusters")
DefaultAssay(scrna) <- assay.used
## assay to disk
if (!ALLINONE){
fname = file.path(SAVE_DIR, "assays", "decontX.Rds")
# construct list
assay_info <- list(
name = "decontX",
assay = scrna[["decontX"]],
fname = fname,
meta = scrna@meta.data,
info = "abundance of ambient RNA")
#save to disk
save_object(assay_info, fname, file_format = COMPRESSION_FORMAT)
if ("assay_info" %ni% names(scrna@tools)){
scrna@tools[["assay_info"]] <- list()
}
scrna@tools[["assay_info"]][["decontX"]] <- fname
## remove from memory
rm(assay_info)
scrna[["decontX"]] <- NULL
gc()
}
return(scrna)
},
error = function(cond) {
ret_code <<- -1
logger.error(cond)
logger.error(traceback())
},
finally={
return(list(scrna, ret_code))
})
return(list(scrna, ret_code))
}
generate_scrna_phase_singleton <- function(scrna){
ret_code <- 0
pconf <- configr::read.config("static/phase.ini")
pconf <- pconf[["phase_singleton"]]
pconf <- pconf[pconf== 1]
for (key in names(pconf)){
f_name = paste("generate_", key, sep="")
f_call = paste(f_name, "(scrna)", sep="")
logger.info(paste("executing", f_name))
ret_list <- eval(parse(text=f_call))
scrna <- ret_list[[1]]
ret_code <- ret_list[[2]]
scrna <- sanity_function(scrna, key)
if(ret_code != 0){ debug_save_stop(scrna, key) }
if(key == "scrna_rawdata"){
scrna@tools$parameter[[cur_time]] <- unlist(pa)
scrna@tools$execution[[cur_time]] <- conf
NRds = paste0(key, ".Rds")
save_object(
object = scrna,
file_name = file.path(SAVE_DIR, NRds),
file_format = COMPRESSION_FORMAT
)
}
logger.info(paste("finished", f_name))
}
return(list(scrna, ret_code))
}
generate_scrna_phase_preprocess <- function(scrna){
ret_code <- 0
pconf <- configr::read.config("static/phase.ini")
pconf <- pconf[["phase_preprocess"]]
pconf <- pconf[pconf== 1]
for (key in names(pconf)){
f_name = paste("generate_", key, sep="")
f_call = paste(f_name, "(scrna)", sep="")
logger.info(paste("executing", f_name))
ret_list <- eval(parse(text=f_call))
scrna <- ret_list[[1]]
ret_code <- ret_list[[2]]
scrna <- sanity_function(scrna, key)
if(ret_code != 0){ debug_save_stop(scrna, key) }
if(key == "scrna_rawdata"){
scrna@tools$parameter[[cur_time]] <- unlist(pa)
scrna@tools$execution[[cur_time]] <- conf
NRds = paste0(key, ".Rds")
save_object(
object = scrna,
file_name = file.path(SAVE_DIR, NRds),
file_format = COMPRESSION_FORMAT
)
}
gc()
logger.info(paste("finished", f_name))
}
return(list(scrna, ret_code))
}
generate_scrna_phase_clustering <- function(scrna){
ret_code <- 0
pconf <- configr::read.config("static/phase.ini")
pconf <- pconf[["phase_clustering"]]
pconf <- pconf[pconf== 1]
for (key in names(pconf)){
f_name = paste("generate_", key, sep="")
f_call = paste(f_name, "(scrna)", sep="")
logger.info(paste("executing", f_name))
ret_list <- eval(parse(text=f_call))
scrna <- ret_list[[1]]
ret_code <- ret_list[[2]]
scrna <- sanity_function(scrna, key)
if(ret_code != 0){ debug_save_stop(scrna, key) }
gc()
logger.info(paste("finished", f_name))
}
return(list(scrna, ret_code))
}
generate_scrna_phase_existed_clusters <- function(scrna){
ret_code <- 0
pconf <- configr::read.config("static/phase.ini")
pconf <- pconf[["phase_existed_clusters"]]
pconf <- pconf[pconf== 1]
if(exists("existed_cluster_slots_map")){
nms <- names(existed_cluster_slots_map)
if (length(nms) > 0){
metas <- nms[startsWith(nms, "meta.")]
reductions <- nms[startsWith(nms, "reduction.")]
for(meta in metas){
assertthat::assert_that(existed_cluster_slots_map[meta] %in% names(scrna@meta.data))
meta_slot <- stringr::str_sub(meta, start=(nchar("meta.")+1))
scrna@meta.data[, meta_slot] <- scrna@meta.data[, existed_cluster_slots_map[meta]]
}
for(reduction in reductions){
assertthat::assert_that(existed_cluster_slots_map[reduction] %in% names(scrna@reductions))
reduction_slot <- stringr::str_sub(reduction, start=(nchar("reduction.")+1))
scrna[[reduction_slot]] <- scrna[[existed_cluster_slots_map[reduction]]]
}
}
}
for (key in names(pconf)){
f_name = paste("generate_", key, sep="")
f_call = paste(f_name, "(scrna)", sep="")
logger.info(paste("executing", f_name))
ret_list <- eval(parse(text=f_call))
scrna <- ret_list[[1]]
ret_code <- ret_list[[2]]
scrna <- sanity_function(scrna, key)
if(ret_code != 0){ debug_save_stop(scrna, key) }
gc()
logger.info(paste("finished", f_name))
}
return(list(scrna, ret_code))
}
generate_scrna_phase_comparing <- function(scrna){
ret_code <- 0
pconf <- configr::read.config("static/phase.ini")
pconf <- pconf[["phase_comparing"]]
pconf <- pconf[pconf== 1]
for (key in names(pconf)){
f_name = paste("generate_", key, sep="")
f_call = paste(f_name, "(scrna)", sep="")
logger.info(paste("executing", f_name))
ret_list <- eval(parse(text=f_call))
scrna <- ret_list[[1]]
ret_code <- ret_list[[2]]
scrna <- sanity_function(scrna, key)
if(ret_code != 0){ debug_save_stop(scrna, key) }
gc()
logger.info(paste("finished", f_name))
}
return(list(scrna, ret_code))
}
generate_scrna_filter <- function(scrna){
ret_code = 0
tryCatch(
{
scrna <- subset(scrna, subset = nFeature_RNA > nFeatureRNAfloor &
nFeature_RNA < nFeatureRNAceiling&
nCount_RNA > nCountRNAfloor&
nCount_RNA < nCountRNAceiling &
percent.mt > pct_mitofloor&
percent.mt < pct_mitoceiling &
percent.ribo > pct_ribofloor &
percent.ribo < pct_riboceiling)
},
error=function(cond) {
ret_code <<- -1
logger.error(cond)
logger.error(traceback())
},
finally={
return(list(scrna, ret_code))
})
return(list(scrna, ret_code))
}
## !!!!Dangerous, in this case, once deleted, never recovered
generate_scrna_del_mitogenes <- function(scrna){
return_code = 0
MT_PATTERN = "^mt-"
if(SPECIES == "Human"){ ## Default is Mouse
MT_PATTERN = "^MT-"
}
allgenes <- rownames(scrna)
mitogenes <- allgenes[grepl(MT_PATTERN, allgenes)]
restgenes <- setdiff(allgenes, mitogenes)
scrna <- scrna[restgenes, ]
return(list( scrna, return_code))
}
generate_scrna_preprocess <- function(scrna){
ret_code = 0
tryCatch(
{
scrna[["percent.mt"]] <- PercentageFeatureSet(scrna, pattern = "^mt-|^MT-")
scrna[["percent.ribo"]] <- PercentageFeatureSet(scrna, pattern = "^Rpl|^Rps|^RPL|^RPS")
mt.genes <- grep(pattern = "^mt-|^MT-", x = rownames(x = scrna), value = TRUE)
ribo.genes <- grep("^Rpl|^Rps|^RPL|^RPS", x = rownames(x = scrna), value = TRUE)
scrna[["percent.exclude"]] <- PercentageFeatureSet(scrna, features = c(mt.genes, ribo.genes))
scrna <- NormalizeData(scrna)
scrna <- FindVariableFeatures(scrna, selection.method = "vst")
scrna <- ScaleData(scrna, features = rownames(scrna))
all.genes <- rownames(x = scrna)
s.genes <- paste0("^", cc.genes$s.genes, "$", collapse = "|")
s.genes <- all.genes[grepl(s.genes, all.genes, ignore.case = TRUE)]
g2m.genes <- paste0("^", cc.genes$g2m.genes, "$", collapse = "|")
g2m.genes <- all.genes[grepl(g2m.genes, all.genes, ignore.case = TRUE)]
scrna <- RunPCA(scrna, features = c(s.genes, g2m.genes), reduction.name="RAW_PCA")
},
error=function(cond) {
ret_code <<- -1
logger.error(cond)
logger.error(traceback())
},
finally={
return(list(scrna, ret_code))
})
return(list(scrna, ret_code))
}
#pbmc <- SCTransform(pbmc, vars.to.regress = "percent.mt", verbose = FALSE) ## another way to preprocess the data
generate_scrna_cellcycle <-function(scrna){
ret_code = 0
tryCatch(
{
all.genes <- rownames(x = scrna)
s.genes <- paste0("^", cc.genes$s.genes, "$", collapse = "|")
s.genes <- all.genes[grepl(s.genes, all.genes, ignore.case = TRUE)]
g2m.genes <- paste0("^", cc.genes$g2m.genes, "$", collapse = "|")
g2m.genes <- all.genes[grepl(g2m.genes, all.genes, ignore.case = TRUE)]
scrna <- NormalizeData(scrna)
scrna <- ScaleData(scrna, features = rownames(scrna))
scrna <- RunPCA(scrna, features = c(s.genes, g2m.genes), reduction.name="BCELLCYCLE_PCA")
scrna <- CellCycleScoring(scrna, s.features = s.genes, g2m.features = g2m.genes, set.ident = TRUE)
scrna$G1.Score = 1 - scrna$S.Score - scrna$G2M.Score
scrna$CC.Difference <- scrna$S.Score - scrna$G2M.Score
if("percent.mt" %ni% names(scrna@meta.data)){ ## for existed clusters analysis
scrna[["percent.mt"]] <- PercentageFeatureSet(scrna, pattern = "^mt-|^MT-")
}
if("percent.ribo" %ni% names(scrna@meta.data)){
scrna[["percent.ribo"]] <- PercentageFeatureSet(scrna, pattern = "^Rpl|^Rps|^RPL|^RPS")
}
},
error=function(cond) {
ret_code <<- -1
logger.error(cond)
logger.error(traceback())
},
finally={
return(list(scrna, ret_code))
})
return(list(scrna, ret_code))
}
generate_scrna_cycleRegressOut <- function(scrna){
ret_code = 0
tryCatch(
{
all.genes <- rownames(x = scrna)
s.genes <- paste0("^", cc.genes$s.genes, "$", collapse = "|")
s.genes <- all.genes[grepl(s.genes, all.genes, ignore.case = TRUE)]
g2m.genes <- paste0("^", cc.genes$g2m.genes, "$", collapse = "|")
g2m.genes <- all.genes[grepl(g2m.genes, all.genes, ignore.case = TRUE)]
### Scale data add exclude
scrna$CC.Difference <- scrna$S.Score - scrna$G2M.Score
scrna <- ScaleData(scrna, vars.to.regress = c("G2M.Score", "S.Score"), features = rownames(scrna))
scrna <- RunPCA(scrna, features = c(s.genes, g2m.genes), nfeatures.print = 10, reduction.name="CELLCYCLED_PCA")
},
error=function(cond) {
ret_code <<- -1
logger.error(cond)
logger.error(traceback())
},
finally={
return(list(scrna, ret_code))
})
return(list(scrna, ret_code))
}
## Regressout cell cycle and mitochondrial
generate_scrna_regressOut<- function(scrna){
ret_code = 0
tryCatch(
{
scrna <- ScaleData(scrna, vars.to.regress = c("nCount_RNA", get_regressout_vector()), features = rownames(scrna))
scrna <- RunPCA(scrna, features = VariableFeatures(scrna), nfeatures.print = 10, reduction.name="RegressOut_PCA")
},
error=function(cond) {
ret_code <<- -1
logger.error(cond)
logger.error(traceback())
},
finally={
return(list(scrna, ret_code))
})
return(list(scrna, ret_code))
}
select_features <- function(data.list){
ret_code = 0
tryCatch(
{
data.list <- SplitObject(scrna, split.by = "name")
data.list <- lapply(X = data.list, FUN = function(x) {
x <- NormalizeData(x, verbose = FALSE)
x <- FindVariableFeatures(x, verbose = FALSE)})
features <- SelectIntegrationFeatures(object.list = data.list)
},
error=function(cond) {
ret_code <<- -1
logger.error(cond)
logger.error(traceback())
},
finally={